MRI Echo Data Synthesis for Noise Reduction
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Solution Overview
Problem
Magnetic Resonance Imaging (MRI) is limited by noise, which affects the signal-to-noise ratio (SNR) and requires long scan times to achieve desired image quality, leading to trade-offs between SNR, scan time, and image quality.
Innovation Solution
The technique involves acquiring multiple MRI images at different echo times, performing curve-fitting using methods like exponential decay or singular value decomposition (SVD) to synthesize noise-free images, and applying filters to reduce noise while maintaining spatial resolution and contrast.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple echo data sets are acquired to improve SNR, then signal-to-noise ratio increases, but scan time increases
Solution Approach 1:
The patent combines multiple echo data sets acquired at different echo times into a single synthesized image. By merging the information from multiple echoes through curve-fitting and synthesis techniques, the method achieves SNR improvement equivalent to multiple averages without proportionally increasing scan time, as the combining process extracts maximum information from the acquired data.
Solution Approach 2:
The patent performs curve-fitting to a specified variation (such as exponential decay) during the data processing phase before final image synthesis. This preliminary modeling of signal behavior allows for optimal combination of echo data sets, enabling SNR enhancement without requiring additional acquisition time beyond what was already spent collecting the multiple echoes.
2Measurement precision
If noise reduction techniques are applied to improve image quality, then SNR increases, but spatial resolution and contrast may degrade
Solution Approach 1:
The patent applies curve-fitting and synthesis techniques locally to each pixel or voxel in the image data. By modeling the signal variation with echo time at each spatial location individually, the method preserves local tissue characteristics and contrast information while reducing noise. This localized approach ensures that spatial resolution and contrast are maintained because the synthesis process adapts to the specific signal behavior at each location rather than applying a uniform filtering operation that could blur details.
Data Source
AI summary
Techniques for magnetic resonance imaging (MRI) including obtaining a plurality of MRI images acquired at different echo times subsequent to an excitation pulse applied to a sample which is being imaged, performing a curve-fitting for a specified variation in each pixel of the MRI images, and using fitted parameters for the specified variation in the MRI images to synthesize the MRI images to form an image at any echo time with reduced noise. Performing singular value decomposition to determine the types of variation in each pixel of the MRI images and using only the most significant variations to synthesize the MRI images to form an image with reduced noise.